Article: Machine learning, but a change in attitude

Introduction

If the benefits of using big data are everything they are supposed to be, why isn’t it being used more widely within the Social Housing sector? Big data methods have the power and potential to significantly reduce some of the bigger risks facing the sector such as greater levels of arrears and abandonments following the roll out of Universal Credit. This might involve, for example, pinpointing those tenants most likely to need help before their difficulties become too great to resolve. But the perception of big data is that it comes with a high price tag and, unless the data is in stellar condition, will probably deliver little that’s of any use. However, as this case study demonstrates, the reality can be very different. Big data methods can be accessible, affordable and highly effective even with limitations within the data. And as a result, perceptions and attitudes are beginning to change. The key requirement however is to lose the word ‘big’ and to start to think small.

A common problem

When it comes to spotting tenants who might be in difficulty many systems already do much to help. They might flag that a rent payment has been missed. An experienced Colleague can then search all the records for that tenant for signs of a growing problem. But it’s not unusual for tenants to miss payments. This might mean that a large number of records have to be searched to find the few tenants in need of help. Many Housing Associations are, however, finding it difficult to step beyond this point. They want to be able to scan data quickly for the relevant clues, but their data takes them only part of the way. It may, for example, show a link between potential payment difficulties and a combination of a tenant’s post code, payment method and tenancy type. This is unlikely to be accurate enough for the task in hand. More work would be needed to trawl through the pool of tenants who possess these characteristics to find the few that really matter – a time-consuming and costly exercise. One option is to wait for the data to improve. But the need for better predictions is pressing. Methods must therefore be found that carry a high chance of success, that can cope with the data quality issues, and crucially, that are affordable.

Be clear about the benefits

A predictive analytics project is underway within one of the leading members of the G15 Group of Housing Associations. The outcome so far is a very short ‘shortlist’ of tenants who are most likely to have abandoned their home, or who are very close to doing so. Not many people opt to simply walk away. But the number will probably grow. And those that do may face a very uncertain future as a result. It’s also a headache for the Housing Association. It can take time for clues to surface to suggest what’s happened. In the meantime the property sits empty, suffers neglect, and arrears build. As a result of this work:

Properties confirmed as empty are recovered and re-let with far less delay. The reduction in rent losses alone is making the exercise worthwhile.

Tenants at risk of leaving have been contacted and helped. Some of their arrears have also been recovered.

And, crucially, confidence is building in the use of data and analytics to tackle critical business problems. The benefits from this initiative are clearly visible and costs have been kept to a minimum. There is therefore a healthy Return On Investment (ROI) to the business.

Preparations are underway to now find tenancies not yet in any apparent difficulty but who are most at risk of falling into significant arrears.

Then focus upon the cost

There are two features of the project that made all the difference to its cost:

The strength of the prediction;

Having all the work performed by in-house staff.

1. Strength of the prediction – the move from big to small

The result needed to pinpoint, out of a population of tens of thousands of tenancies, just the few at greatest risk of abandonment. If the pool of candidates had been too big then the additional effort needed to find the few that mattered would have caused costs to spiral. Traditional big data methods were not going to work. They might yield some interesting correlations but not to the level of accuracy required. To find just the few who mattered a small amount of highly relevant data was needed. To locate it the experts in the field (e.g. Neighbourhood Officers) were consulted. Whenever they were tasked with clarifying whether or not a home had been abandoned they would look for clues within a range of records across three or four different systems. They were asked to list those clues and indicate within which records they could be found. For example Each clue translated into a data extraction task. For example, to separate out evidence of direct contact with the tenant text notes from three different systems had to be split apart. Some of the work was complex however the outcomes were of immediate value. When all the relevant data was combined and made easy to read the few tenants being sought jumped into view. Most importantly the list was small. This allowed those tenants in greatest need of help to be found and contacted with the minimum of delay. Jenny and Michael were two of those found. “Jenny lives with her teenage son in Derby. Her working hours were reduced to 1 day per week. She was then told that she had been paid too much Housing Benefit. Her benefits would therefore be reduced until the account balanced once more.Unsurprisingly Jenny’s arrears started to mount. Her mobile number then ceased to work, gas safety contractors failed to get access, and cards through the door asking Jenny to make contact went unanswered.She was found in time and with help from the Welfare Team was able to stay.Michael lives alone in South London. His rent payments had been sporadic and occasionally he had fallen into arrears. When this happened he received reminders to pay to which he had always responded well.But he then did something not seen before. Whilst his rent account was still in credit, he phoned to check the balance. He then paid his rent for that month in full bringing his account further into the black.But he then stopped paying, and it appeared to be for good. He was found, he seemed to be ok, but he departed soon after.” Benefits high, costs low. Small data won the day.

2. Have all the work performed by in-house staff

Those searching for abandonees are all in-house staff. They are becoming increasingly adept at addressing issues with analytics and ever more inventive when extracting value from the data in the business. They needed training and support to get them going. But they are now preparing to extend their approach to find tenants with the potential to grow arrears without warning. The tools needed were already within the business, or available free of charge. The training and support focused upon the following:

The root causes for the business problem and the data needed to solve it;

How to keep costs to a minimum;

The type of analytics and the knowledge and skill needed;

Communication and understanding throughout the business about the problems, the aims, and the results.

No prior experience or particular credentials were needed. But it helped to have people who are critical thinkers, who have a high level of curiosity, and who are able to tap into the right areas of expertise across the business.#

Use the power of prediction to shift mind-sets

The most important discipline within the project was to think the problem through and to understand it fully before making a start. This ensured that the approach throughout remained as simple and understandable as possible. It would have been very easy to create a complex mathematical model of the world of abandonments only to find that it delivered little of any value. If the people on the ground who needed to act on its recommendations didn’t understand it or trust what it was saying then nothing much would have been achieved. As a result of their experience mind-sets are shifting towards the idea that advanced analytics are achievable, they can deliver significant benefits and they don’t have to cost the earth.

If you would like to know more

We at Develin have helped a large number of organisations across many different sectors to manage their costs down through the use of highly effective analytics. If we can help through direct support, training, or a combination of the two, please don’t hesitate to get in touch.